首页|SeisResoDiff:Seismic resolution enhancement based on a diffusion model

SeisResoDiff:Seismic resolution enhancement based on a diffusion model

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion.Therefore,geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration.Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement,there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence.Hence,we propose to apply diffusion models,among the most popular generative models,to enhance seismic resolution.Specifically,we apply the classic diffusion mod-el-denoising diffusion probabilistic model(DDPM),conditioned on the seismic data in low resolution,to reconstruct corresponding high-resolution images.Herein the entire scheme is referred to as SeisRe-soDiff.To provide a comprehensive and clear understanding of SeisResoDiff,we introduce the basic theories of diffusion models and detail the optimization objective's derivation with the aid of diagrams and algorithms.For implementation,we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells.Subsequently,we apply the trained model to both synthetic and field datasets,evaluating the results in three aspects:the appearance of seismic sections and slices in the time domain,frequency spectra,and comparisons with the synthetic data using real well-logging data at the well locations.The results demonstrate not only effective seismic resolution enhancement,but also additional denoising by the diffusion model.Experimental comparisons indicate that training the model on noisy data,which are more realistic,outperforms training on clean data.The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability,yielding more competitive results compared to our previous research.

Seismic resolution enhancementDiffusion modelHigh resolutionReservoir characterizationDeep learningSeismic data processing

Hao-Ran Zhang、Yang Liu、Yu-Hang Sun、Gui Chen

展开 >

State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China

CNPC Key Laboratory of Geophysical Prospecting,China University of Petroleum(Beijing),Beijing,102249,China

School of Petroleum,China University of Petroleum(Beijing)at Karamay,Karamay,834000,Xinjiang,China

Northeast Petroleum University,Daqing 163318,Heilongjiang China

展开 >

2024

10.1016/j.petsci.2024.07.002

石油科学(英文版)
中国石油大学（北京）

石油科学(英文版)

EI
影响因子：0.88
ISSN：1672-5107
年,卷(期)：2024.21(5)